By the time a consumer reaches a bank or insurer’s website, AI may already have shaped their shortlist. Financial service providers (FSPs) are now competing for inclusion, accuracy and trust in the answers guiding those decisions. The solution may lie in hard-won lessons from local retail brands.
According to the EY Global AI Sentiment Survey, AI adoption for personal finance has grown significantly across global markets. The research shows that nearly half (49%) of respondents have used AI to support savings and investment decisions in the past six months. Around 21% report using AI agents for financial product recommendations, and 18% say they use AI for budgeting, household finance management and trading support.
More importantly, a full 14% of respondents say they have “allowed AI to select financial services providers on their behalf, while 11% say they have deferred to AI to manage their finances with little or no human intervention”.
Imraan Abrahams, senior SEO specialist at Incubeta described the shift as “discovery via choice to discovery via recommendation,” adding that instead of sorting through links themselves, consumers increasingly rely on an AI-generated answer to shortlist options.
“AI is now acting like the intermediary to the entire customer journey,” explains Michelle Goosen, business unit director at Incubeta. “That raises the stakes for a brand’s product information. The battleground is no longer just visibility, it’s whether the information AI can access is good enough to support consideration.”
Taking a page out of Retail’s book
Retail offers FSPs a useful lesson in how to answer the questions customers actually ask.
People are not only searching for product names, they also want to know what an option would mean in their particular circumstances. In financial services, that can mean the real monthly cost of a loan, whether switching providers is worth the effort, what fees apply, or how exclusions affect a claim. Customers may also ask about comparisons between products and the tax implications of retirement-savings decisions, for example.
These are questions that require more than polished promotional copy.
Abrahams says FSPs should favour “clarity, facts and exact figures over marketing-heavy language. “Useful pages should explain costs, terms, eligibility and limitations plainly, with structured FAQs and credible sources to support important claims,” he says.
Technical foundations also matter and schema markup can help search systems interpret details such as interest rates, licence information and product terms.
“Financial education is also important, because customers increasingly want someone to help them make sense of their options rather than just give them product information. The key shift is from What do we want to tell customers? to What questions are customers actually asking?” Goosen adds.
Get the numbers right
Financial products change, and content that was once correct can become misleading if rates, fees or conditions are out of date. Providers should keep their own channels current and check that key product details are consistent across comparison websites and other third-party sources.
“There also needs to be strong governance behind content management. Someone needs to own that information, know when it was last reviewed and make sure it gets updated when the product changes,” Goosen explains. “In financial services, being easy for AI to understand is only useful if the information is also safe, accurate and compliant.”
Personalise without being creepy
Recommendations can reduce choice overload, but in financial services the use of personal information can quickly feel intrusive. Goosen contrasts familiar retail signals, such as shopping habits, with sensitive financial circumstances such as debt.
“The first rule is don’t be creepy. For FSPs, relevant personalisation should be transparent and based on information customers knowingly provide. It should help people understand suitable options without making unsupported assumptions about their income or needs, and leave them in control of the decision,” she says.
Lessons in measurement
Rankings, impressions, clicks and conversions still matter. They show whether a site is visible and whether people are visiting it. But they won’t, on their own, show whether a brand is appearing in AI answers or being described correctly.
Abrahams identifies AI share of voice, citation rate, citation accuracy and AI referral traffic as additional measures. “In a regulated category, citation accuracy is particularly important. A brand mention is not a success if the AI has misstated a fee, rate or exclusion,” he says.
The specialists agree that the lesson isn't to copy retail's tactics wholesale, it's to copy its customer-centricity while keeping a much stronger layer of governance, transparency and suitability.
“The winners won't necessarily be the FSPs with the most AI. They'll be the ones whose products, information and customer journeys are easiest for both humans and AI to understand, compare and trust,” Goosen advises.
